Core C: Data Management and Statistical Core

摘要（核心C） 数据管理和统计核心（核心C）与ADRC的所有核心和项目一起工作， 用于健全数据收集的系统和过程，包括识别和跟踪所有形式， 生物标本和图像以及评估和计算产品来自这些主要 材料.所有计算机化材料的收集都采用最先进的方法加以保护。记录 在整个处理过程中被去除身份。数据管理创建和管理所有ADRC之间的数据流 参与者、合作伙伴和项目。所有研究者将随时获得完整准确的数据 使用现代数据管理技术、架构、设计和工具。ADRC将收集标准 国家阿尔茨海默氏症协调中心（NACC）数据元素，并根据需要报告这些元素。 NACC软件将用于验证数据输入。验证后，将连接NACC数据元素 与ADRC数据元素一起创建统一的ADRC数据库。分析数据集将从 根据需要建立统一的数据库，以支持整个项目和核心的调查。统计专业知识 确保为所有研究和项目创建高效和有效的设计，包括功率分析， 证明调查关键研究问题的样本大小。所有测量仪器都要进行审查， 有效性和准确性。数据聚合、摘要、表格和可视化都是使用 确保数据清晰呈现的做法。使用统计方法，以确保准确的估计和 合理的推论纵向混合模型将用于解决与疾病进展相关的问题 以及观察到的进展与可能的生物标志物的关系。

SUMMARY (Core C) The Data Management and Statistical Core (Core C) works with all cores and projects of the ADRC to create systems and processes for sound data collection including identification and tracking of all forms, biospecimens and images as well as the assessments and computational products derived from these primary materials. The collection of all computerized materials is secured using state-of-the-art methods. Records are de-identified throughout processing. Data Management creates and manages data flows between all ADRC participants, partners and projects. Complete and accurate data will be available to all investigators at all times using modern data management techniques, architectures, designs and tools. The ADRC will collect standard National Alzheimer s Coordinating Center (NACC) data elements and report those elements as required. NACC software will be used to validate data entry. Following validation, NACC data elements will be joined with ADRC data elements to create a unified ADRC database. Analysis datasets will be created from the unified database on demand to support investigations throughout the projects and cores. Statistical expertise insures that efficient and effective designs are created for all studies and projects, including power analyses to justify sample sizes for investigating key research questions. All measurement instruments are reviewed for validity and accuracy. Data aggregation, summaries, tabulations and visualizations are created using best practices to insure clear presentation of data. Statistical methods are used that insure accurate estimates and sound inferences. Longitudinal mixed models will be used to address issues related to progression of disease and the relationship of observed progression to possible biomarkers.